JMIR Publications

ABSTRACT

Background:

Cognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life and autonomy of affected individuals. As the population ages, this will place enormous strain on healthcare and economic systems. While computerized cognitive training programs have demonstrated some promise in addressing cognitive decline, adherence to these interventions can be challenging.

Objective:

The objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailored adherence support systems to promote engagement with cognitive training among older adults.

Methods:

Data from two previously conducted cognitive training intervention studies were used to forecast adherence levels among older participants. Deep Convolutional Neural Networks (CNNs) were employed to leverage their feature learning capabilities and predict adherence patterns based on past behavior. Domain Adaptation (DA) was used to address the challenge of limited training data for each participant, by utilizing data from other participants with similar playing patterns. Time series data was converted into image format using Gramian Angular Fields (GAFs), to facilitate clustering of participants during domain adaptation. To the best of our knowledge, this is the first effort to use domain adaptation techniques to predict older adults’ daily adherence to cognitive training programs.

Results:

Our results demonstrated the promise and potential of deep neural networks and domain adaptation for predicting adherence lapses. In all three studies, using two independent datasets, domain adaptation consistently produced the best accuracy values.

Conclusions:

Our findings highlight that deep learning and domain adaptation techniques can aid in the development of adherence support systems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, and wellbeing. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancing the quality of life of individuals at risk for cognitive impairments. This research informs the development of more effective interventions, benefiting individuals and society by improving conditions associated with aging.